Light gradient boosting machine (LightGBM), proposed and developed by Microsoft [1]}, uses gradient-based one-side sampling (GOSS) to filter out data instances on the basis of their contribution to the gradient of the loss function. The best split is obtained by using all of the instances with large gradients and a random sample of instances with small gradients to maintain a balance between the training data reduction and accuracy. LightGBM uses a leaf-wise tree growth mechanism which allows the growth of an imbalanced tree.
